Overview

Dataset statistics

Number of variables15
Number of observations5698
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory667.9 KiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
recency_days is highly correlated with qnt_purchasesHigh correlation
qnt_purchases is highly correlated with gross_revenue and 4 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
freq_purchase is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qtd_returned is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
freq_returns is highly correlated with qtd_returned and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_variety is highly correlated with var_products and 1 other fieldsHigh correlation
item_rp_ratio is highly correlated with qtd_returned and 2 other fieldsHigh correlation
net_margin is highly correlated with qtd_returned and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 1 other fieldsHigh correlation
var_products is highly correlated with avg_basket_varietyHigh correlation
qnt_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_basket_varietyHigh correlation
avg_basket_variety is highly correlated with var_products and 1 other fieldsHigh correlation
item_rp_ratio is highly correlated with net_marginHigh correlation
net_margin is highly correlated with item_rp_ratioHigh correlation
gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
freq_purchase is highly correlated with qnt_purchasesHigh correlation
qtd_returned is highly correlated with freq_returns and 2 other fieldsHigh correlation
freq_returns is highly correlated with qtd_returned and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_basket_variety is highly correlated with var_productsHigh correlation
item_rp_ratio is highly correlated with qtd_returned and 2 other fieldsHigh correlation
net_margin is highly correlated with qtd_returned and 2 other fieldsHigh correlation
df_index is highly correlated with customer_id and 1 other fieldsHigh correlation
customer_id is highly correlated with df_index and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 4 other fieldsHigh correlation
recency_days is highly correlated with df_index and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 3 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 5 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtd_returned is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_basket_variety is highly correlated with customer_id and 2 other fieldsHigh correlation
item_rp_ratio is highly correlated with net_marginHigh correlation
net_margin is highly correlated with item_rp_ratioHigh correlation
gross_revenue is highly skewed (γ1 = 23.64230131) Skewed
qnt_items is highly skewed (γ1 = 25.34473112) Skewed
avg_ticket is highly skewed (γ1 = 48.12570503) Skewed
qtd_returned is highly skewed (γ1 = 24.95562219) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
customer_id has unique values Unique
qtd_returned has 4198 (73.7%) zeros Zeros
freq_returns has 4198 (73.7%) zeros Zeros
item_rp_ratio has 4198 (73.7%) zeros Zeros

Reproduction

Analysis started2021-10-23 01:50:45.776909
Analysis finished2021-10-23 01:51:08.069973
Duration22.29 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5698
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2897.338189
Minimum0
Maximum5788
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:08.138886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile289.85
Q11455.25
median2899.5
Q34342.75
95-th percentile5497.15
Maximum5788
Range5788
Interquartile range (IQR)2887.5

Descriptive statistics

Standard deviation1669.76685
Coefficient of variation (CV)0.5763106485
Kurtosis-1.196205631
Mean2897.338189
Median Absolute Deviation (MAD)1444
Skewness-0.003581351907
Sum16509033
Variance2788121.335
MonotonicityStrictly increasing
2021-10-22T22:51:08.233556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
38471
 
< 0.1%
38671
 
< 0.1%
38661
 
< 0.1%
38651
 
< 0.1%
38641
 
< 0.1%
38631
 
< 0.1%
38621
 
< 0.1%
38611
 
< 0.1%
38601
 
< 0.1%
Other values (5688)5688
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57881
< 0.1%
57871
< 0.1%
57861
< 0.1%
57851
< 0.1%
57841
< 0.1%
57831
< 0.1%
57821
< 0.1%
57811
< 0.1%
57801
< 0.1%
57791
< 0.1%

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5698
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16600.64812
Minimum12347
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:08.332247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12699.55
Q114287.25
median16226.5
Q318211.75
95-th percentile21725.15
Maximum22709
Range10362
Interquartile range (IQR)3924.5

Descriptive statistics

Standard deviation2809.23708
Coefficient of variation (CV)0.1692245423
Kurtosis-0.8230600484
Mean16600.64812
Median Absolute Deviation (MAD)1964
Skewness0.4410586186
Sum94590493
Variance7891812.973
MonotonicityNot monotonic
2021-10-22T22:51:08.431943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
179971
 
< 0.1%
178911
 
< 0.1%
164981
 
< 0.1%
137451
 
< 0.1%
155841
 
< 0.1%
210891
 
< 0.1%
210881
 
< 0.1%
210871
 
< 0.1%
210861
 
< 0.1%
Other values (5688)5688
99.8%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
123571
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5454
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1735.645921
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:08.532431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.2355
Q1236.4925
median612.07
Q31567.645
95-th percentile5228.677
Maximum279138.02
Range279137.6
Interquartile range (IQR)1331.1525

Descriptive statistics

Standard deviation7432.526681
Coefficient of variation (CV)4.282282803
Kurtosis729.0754875
Mean1735.645921
Median Absolute Deviation (MAD)477.79
Skewness23.64230131
Sum9889710.46
Variance55242452.87
MonotonicityNot monotonic
2021-10-22T22:51:08.620588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
2.958
 
0.1%
1.258
 
0.1%
4.958
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
3.757
 
0.1%
4.256
 
0.1%
7.56
 
0.1%
5.956
 
0.1%
Other values (5444)5626
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
70100.491
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.8555634
Minimum0
Maximum373
Zeros37
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:08.713685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3199
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)176

Descriptive statistics

Standard deviation111.5314761
Coefficient of variation (CV)0.9544387354
Kurtosis-0.6378758846
Mean116.8555634
Median Absolute Deviation (MAD)61
Skewness0.8154086444
Sum665843
Variance12439.27016
MonotonicityNot monotonic
2021-10-22T22:51:08.807399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110
 
1.9%
4105
 
1.8%
398
 
1.7%
292
 
1.6%
1086
 
1.5%
882
 
1.4%
1779
 
1.4%
979
 
1.4%
778
 
1.4%
1567
 
1.2%
Other values (294)4822
84.6%
ValueCountFrequency (%)
037
 
0.6%
1110
1.9%
292
1.6%
398
1.7%
4105
1.8%
552
0.9%
778
1.4%
882
1.4%
979
1.4%
1086
1.5%
ValueCountFrequency (%)
37323
0.4%
37222
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36615
0.3%
36519
0.3%
36411
0.2%
3627
 
0.1%

qnt_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.47034047
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:08.905875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.808567324
Coefficient of variation (CV)1.961930647
Kurtosis302.6603825
Mean3.47034047
Median Absolute Deviation (MAD)0
Skewness13.20268773
Sum19774
Variance46.356589
MonotonicityNot monotonic
2021-10-22T22:51:08.995463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12870
50.4%
2829
 
14.5%
3503
 
8.8%
4394
 
6.9%
5237
 
4.2%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
1055
 
1.0%
Other values (46)332
 
5.8%
ValueCountFrequency (%)
12870
50.4%
2829
 
14.5%
3503
 
8.8%
4394
 
6.9%
5237
 
4.2%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
1055
 
1.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
< 0.1%
861
< 0.1%
721
< 0.1%
621
< 0.1%
602
< 0.1%
571
< 0.1%

var_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct438
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.47929098
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:09.086408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q113
median36
Q384
95-th percentile241
Maximum1786
Range1785
Interquartile range (IQR)71

Descriptive statistics

Standard deviation100.7637571
Coefficient of variation (CV)1.450270371
Kurtosis43.72624843
Mean69.47929098
Median Absolute Deviation (MAD)28
Skewness4.650616344
Sum395893
Variance10153.33475
MonotonicityNot monotonic
2021-10-22T22:51:09.179425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1277
 
4.9%
2149
 
2.6%
3112
 
2.0%
10101
 
1.8%
597
 
1.7%
996
 
1.7%
1193
 
1.6%
693
 
1.6%
893
 
1.6%
790
 
1.6%
Other values (428)4497
78.9%
ValueCountFrequency (%)
1277
4.9%
2149
2.6%
3112
2.0%
490
 
1.6%
597
 
1.7%
693
 
1.6%
790
 
1.6%
893
 
1.6%
996
 
1.7%
10101
 
1.8%
ValueCountFrequency (%)
17861
< 0.1%
17661
< 0.1%
13221
< 0.1%
11181
< 0.1%
8841
< 0.1%
8171
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7171
< 0.1%

qnt_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1835
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean943.7620218
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:09.277182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1106
median317
Q3801.75
95-th percentile2894.75
Maximum196844
Range196843
Interquartile range (IQR)695.75

Descriptive statistics

Standard deviation4173.345102
Coefficient of variation (CV)4.422031196
Kurtosis956.6808892
Mean943.7620218
Median Absolute Deviation (MAD)253
Skewness25.34473112
Sum5377556
Variance17416809.34
MonotonicityNot monotonic
2021-10-22T22:51:09.373832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1113
 
2.0%
273
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1225
 
0.4%
8822
 
0.4%
7221
 
0.4%
720
 
0.4%
Other values (1825)5260
92.3%
ValueCountFrequency (%)
1113
2.0%
273
1.3%
351
0.9%
449
0.9%
535
 
0.6%
629
 
0.5%
720
 
0.4%
818
 
0.3%
97
 
0.1%
1017
 
0.3%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

avg_ticket
Real number (ℝ≥0)

SKEWED

Distinct5505
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.03434847
Minimum0.42
Maximum13305.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:09.470310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile3.460567302
Q17.95
median15.85435606
Q321.96158411
95-th percentile75.16291917
Maximum13305.5
Range13305.08
Interquartile range (IQR)14.01158411

Descriptive statistics

Standard deviation210.7747768
Coefficient of variation (CV)6.791661083
Kurtosis2837.677304
Mean31.03434847
Median Absolute Deviation (MAD)7.490555851
Skewness48.12570503
Sum176833.7176
Variance44426.00652
MonotonicityNot monotonic
2021-10-22T22:51:09.569412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7511
 
0.2%
4.9510
 
0.2%
2.959
 
0.2%
1.259
 
0.2%
7.958
 
0.1%
12.757
 
0.1%
8.257
 
0.1%
1.657
 
0.1%
4.156
 
0.1%
3.356
 
0.1%
Other values (5495)5618
98.6%
ValueCountFrequency (%)
0.423
0.1%
0.5351
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.83714285711
 
< 0.1%
0.842
< 0.1%
0.853
0.1%
1.0022222221
 
< 0.1%
1.021
 
< 0.1%
1.038751
 
< 0.1%
ValueCountFrequency (%)
13305.51
< 0.1%
4599.681
< 0.1%
38611
< 0.1%
3202.921
< 0.1%
30961
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%
1001.21
< 0.1%
952.98751
< 0.1%
931.51
< 0.1%

freq_purchase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1226
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5475501672
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:09.663977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.01104667696
Q10.02494806094
median1
Q31
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.9750519391

Descriptive statistics

Standard deviation0.5508335323
Coefficient of variation (CV)1.005996464
Kurtosis138.4768319
Mean0.5475501672
Median Absolute Deviation (MAD)0
Skewness4.845559381
Sum3119.940853
Variance0.3034175803
MonotonicityNot monotonic
2021-10-22T22:51:09.757439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12878
50.5%
249
 
0.9%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238116
 
0.3%
0.0833333333315
 
0.3%
0.0344827586215
 
0.3%
0.0909090909115
 
0.3%
0.0294117647114
 
0.2%
0.0212765957413
 
0.2%
Other values (1216)2648
46.5%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
< 0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
249
 
0.9%
1.1428571431
 
< 0.1%
12878
50.5%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

qtd_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct210
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.14987715
Minimum0
Maximum6504
Zeros4198
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:09.854742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile38
Maximum6504
Range6504
Interquartile range (IQR)1

Descriptive statistics

Standard deviation154.3255531
Coefficient of variation (CV)9.555834491
Kurtosis807.125368
Mean16.14987715
Median Absolute Deviation (MAD)0
Skewness24.95562219
Sum92022
Variance23816.37634
MonotonicityNot monotonic
2021-10-22T22:51:09.945327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04198
73.7%
1169
 
3.0%
2151
 
2.7%
3105
 
1.8%
489
 
1.6%
678
 
1.4%
561
 
1.1%
1252
 
0.9%
744
 
0.8%
843
 
0.8%
Other values (200)708
 
12.4%
ValueCountFrequency (%)
04198
73.7%
1169
 
3.0%
2151
 
2.7%
3105
 
1.8%
489
 
1.6%
561
 
1.1%
678
 
1.4%
744
 
0.8%
843
 
0.8%
941
 
0.7%
ValueCountFrequency (%)
65041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%
15352
< 0.1%

freq_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct427
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1616901892
Minimum0
Maximum4
Zeros4198
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:10.041210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.009901311518
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0.009901311518

Descriptive statistics

Standard deviation0.3730391124
Coefficient of variation (CV)2.30712274
Kurtosis4.862729409
Mean0.1616901892
Median Absolute Deviation (MAD)0
Skewness2.20137007
Sum921.310698
Variance0.1391581793
MonotonicityNot monotonic
2021-10-22T22:51:10.135787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04198
73.7%
1853
 
15.0%
214
 
0.2%
0.58
 
0.1%
0.28571428578
 
0.1%
0.025641025647
 
0.1%
0.256
 
0.1%
0.0094786729865
 
0.1%
0.019607843145
 
0.1%
0.012987012995
 
0.1%
Other values (417)589
 
10.3%
ValueCountFrequency (%)
04198
73.7%
0.0055710306411
 
< 0.1%
0.0056818181822
 
< 0.1%
0.0058651026391
 
< 0.1%
0.0059347181011
 
< 0.1%
0.0059523809521
 
< 0.1%
0.0060240963861
 
< 0.1%
0.0060422960731
 
< 0.1%
0.0061728395061
 
< 0.1%
0.0061919504641
 
< 0.1%
ValueCountFrequency (%)
41
 
< 0.1%
31
 
< 0.1%
214
 
0.2%
1853
15.0%
0.751
 
< 0.1%
0.66666666674
 
0.1%
0.58
 
0.1%
0.42857142861
 
< 0.1%
0.44
 
0.1%
0.33333333331
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2365
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.2348805
Minimum1
Maximum7824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:10.234252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median152
Q3290
95-th percentile731.15
Maximum7824
Range7823
Interquartile range (IQR)215

Descriptive statistics

Standard deviation345.1539697
Coefficient of variation (CV)1.424873119
Kurtosis83.50967642
Mean242.2348805
Median Absolute Deviation (MAD)96.5
Skewness6.639402785
Sum1380254.349
Variance119131.2628
MonotonicityNot monotonic
2021-10-22T22:51:10.330448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114
 
2.0%
272
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1226
 
0.5%
7222
 
0.4%
10022
 
0.4%
7321
 
0.4%
Other values (2355)5257
92.3%
ValueCountFrequency (%)
1114
2.0%
272
1.3%
351
0.9%
3.3333333331
 
< 0.1%
449
0.9%
535
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
629
 
0.5%
6.1428571431
 
< 0.1%
ValueCountFrequency (%)
78241
< 0.1%
59631
< 0.1%
45071
< 0.1%
43001
< 0.1%
42821
< 0.1%
42801
< 0.1%
41361
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
30281
< 0.1%

avg_basket_variety
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1169
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.05971538
Minimum0.2
Maximum748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:10.429662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17.285714286
median15.07417582
Q331
95-th percentile173
Maximum748
Range747.8
Interquartile range (IQR)23.71428571

Descriptive statistics

Standard deviation75.5306435
Coefficient of variation (CV)2.038079427
Kurtosis28.43551958
Mean37.05971538
Median Absolute Deviation (MAD)9.925824176
Skewness4.854137488
Sum211166.2582
Variance5704.878107
MonotonicityNot monotonic
2021-10-22T22:51:10.522351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1276
 
4.8%
2161
 
2.8%
3115
 
2.0%
9105
 
1.8%
10105
 
1.8%
8103
 
1.8%
7101
 
1.8%
6101
 
1.8%
5100
 
1.8%
1397
 
1.7%
Other values (1159)4434
77.8%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
0.1%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.2%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7031
< 0.1%
6861
< 0.1%
6751
< 0.1%
6731
< 0.1%
6601
< 0.1%
6491
< 0.1%
6471
< 0.1%

item_rp_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1379
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01201326554
Minimum0
Maximum1
Zeros4198
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:10.620078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.001033139699
95-th percentile0.04109589041
Maximum1
Range1
Interquartile range (IQR)0.001033139699

Descriptive statistics

Standard deviation0.06471766549
Coefficient of variation (CV)5.387183467
Kurtosis134.7358715
Mean0.01201326554
Median Absolute Deviation (MAD)0
Skewness10.5498386
Sum68.45158703
Variance0.004188376226
MonotonicityNot monotonic
2021-10-22T22:51:10.716403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04198
73.7%
111
 
0.2%
0.010752688174
 
0.1%
0.0096618357494
 
0.1%
0.0092592592593
 
0.1%
0.038461538463
 
0.1%
0.02439024393
 
0.1%
0.023809523813
 
0.1%
0.0074626865673
 
0.1%
0.014925373133
 
0.1%
Other values (1369)1463
 
25.7%
ValueCountFrequency (%)
04198
73.7%
0.00011696362431
 
< 0.1%
0.00018399264031
 
< 0.1%
0.00028169014081
 
< 0.1%
0.00031407035181
 
< 0.1%
0.00036192544341
 
< 0.1%
0.00036324010171
 
< 0.1%
0.00036376864311
 
< 0.1%
0.00036710719531
 
< 0.1%
0.0003930817611
 
< 0.1%
ValueCountFrequency (%)
111
0.2%
0.98630136991
 
< 0.1%
0.83333333331
 
< 0.1%
0.63333333331
 
< 0.1%
0.61151079141
 
< 0.1%
0.60088365241
 
< 0.1%
0.59645669291
 
< 0.1%
0.56488549621
 
< 0.1%
0.56463878331
 
< 0.1%
0.56020408161
 
< 0.1%

net_margin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1494
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9849909884
Minimum0
Maximum1
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2021-10-22T22:51:10.812414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.930997443
Q10.9979218471
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.00207815293

Descriptive statistics

Standard deviation0.06715476332
Coefficient of variation (CV)0.06817804844
Kurtosis119.0519217
Mean0.9849909884
Median Absolute Deviation (MAD)0
Skewness-9.742664177
Sum5612.478652
Variance0.004509762237
MonotonicityNot monotonic
2021-10-22T22:51:10.908576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13721
65.3%
1223
 
3.9%
1161
 
2.8%
193
 
1.6%
011
 
0.2%
0.98097273151
 
< 0.1%
0.97919265731
 
< 0.1%
0.97072125231
 
< 0.1%
0.99476653251
 
< 0.1%
0.96312878591
 
< 0.1%
Other values (1484)1484
 
26.0%
ValueCountFrequency (%)
011
0.2%
0.049466537341
 
< 0.1%
0.13368983961
 
< 0.1%
0.14017054081
 
< 0.1%
0.25023409121
 
< 0.1%
0.28295102291
 
< 0.1%
0.3241179911
 
< 0.1%
0.35486018641
 
< 0.1%
0.481
 
< 0.1%
0.48708590681
 
< 0.1%
ValueCountFrequency (%)
1223
 
3.9%
13721
65.3%
1161
 
2.8%
193
 
1.6%
0.99991807691
 
< 0.1%
0.99984316131
 
< 0.1%
0.99972431321
 
< 0.1%
0.99969169041
 
< 0.1%
0.99967287611
 
< 0.1%
0.99961497151
 
< 0.1%

Interactions

2021-10-22T22:51:05.741203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:46.851712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:48.179055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:49.483968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:50.773355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:52.080231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:53.336269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:54.650089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:56.637671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:57.927810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:59.237920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:00.504645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:01.834507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:03.165941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:04.478092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:05.822588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:46.957576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:48.264802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:49.567511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:50.859071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:52.161550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:53.421287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:54.738221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:56.724352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:58.013634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:59.321568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:00.595415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:01.928044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:03.251565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:04.559651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:05.908937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:47.040934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:48.348907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:49.649023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:50.943403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:52.240706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:53.504733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:54.829244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:56.806079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:58.096701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:59.403266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:00.679088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:02.013587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:03.335900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:04.644482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:05.989517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:47.123624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:48.433708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:49.731770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:51.026084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:52.318281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:53.587949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:54.917276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:56.891582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:58.185982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:59.484104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:00.763125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:02.098827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:03.419474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:04.724969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:06.074728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:47.214409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:48.522542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:49.822695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:51.114622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:52.401383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:53.675410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:55.646256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:56.977678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:58.280678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:59.570959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:00.851713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:02.188699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:03.507694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:04.809749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:51:06.157624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:47.293393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:48.603827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T22:50:49.905909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

2021-10-22T22:51:11.001458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-22T22:51:11.140938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-22T22:51:11.280305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-22T22:51:11.419770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-22T22:51:07.811431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-22T22:51:08.004530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_varietyitem_rp_rationet_margin
00178505391.21372.034.021.01733.018.15222217.00000040.01.00000050.9705880.6176470.0230810.980973
11130473232.5956.09.0105.01390.018.9040350.02830235.00.023973154.44444411.6666670.0251800.955611
22125836705.382.015.0114.05028.028.9025000.04032350.00.105263335.2000007.6000000.0099440.988660
3313748948.2595.05.024.0439.033.8660710.0179210.00.00000087.8000004.8000000.0000001.000000
4415100876.00333.03.01.080.0292.0000000.07317122.00.07894726.6666670.3333330.2750000.725000
55152914623.3025.014.061.02102.045.3264710.04011529.00.032468150.1428574.3571430.0137960.984472
66146885630.877.021.0148.03621.017.2197860.057221399.00.019608172.4285717.0476190.1101910.907032
77178095411.9116.012.046.02057.088.7198360.03352041.00.013072171.4166673.8333330.0199320.987609
881531160767.900.091.0567.038194.025.5434640.243316474.00.072193419.7142866.2307690.0124100.977808
99160982005.6387.07.034.0613.029.9347760.0243900.00.00000087.5714294.8571430.0000001.000000

Last rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_varietyitem_rp_rationet_margin
56885779227004839.421.01.055.01074.078.0551611.00.00.01074.055.00.01.0
5689578013298360.001.01.02.096.0180.0000001.00.00.096.02.00.01.0
5690578114569227.391.01.010.079.018.9491671.00.00.079.010.00.01.0
569157822270417.901.01.07.014.02.5571431.00.00.014.07.00.01.0
56925783227053.351.01.02.02.01.6750001.00.00.02.02.00.01.0
56935784227065699.001.01.0634.01747.08.9889591.00.00.01747.0634.00.01.0
56945785227076756.060.01.0730.02010.09.2548771.00.00.02010.0730.00.01.0
56955786227083217.200.01.056.0654.054.5288141.00.00.0654.056.00.01.0
56965787227093950.720.01.0217.0731.018.2060831.00.00.0731.0217.00.01.0
5697578812713794.550.01.037.0505.021.4743241.00.00.0505.037.00.01.0